Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). Methods: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. Results: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). Limitations: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. Conclusion: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

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Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modeling approach

10.31234/osf.io/t2dhn ◽

2020 ◽

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer L. Stewart ◽

James Touthang ◽

Rayus Kuplicki ◽

...

Keyword(s):

Computational Modeling ◽

Psychiatric Disorders ◽

Clinical Sample ◽

Negative Outcomes ◽

Full Sample ◽

Decision Uncertainty ◽

Emotional Conflict ◽

Model Free ◽

Validation Parameters ◽

Approach Avoidance

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g. sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modeling to examine two factors often not distinguished within model-free analyses of AAC: decision uncertainty (DU) and sensitivity to negative outcomes vs. reward (emotional conflict; EC).Methods: A previously-validated AAC task was completed by 477 participants, including healthy controls (HCs; N=59), individuals with substance use disorders (SUDs; N=159) and individuals with depression and/or anxiety (DEP/ANX; N=260) disorders without SUDs. Using an active inference model, we estimated individual-level values for a model parameter (β) reflecting DU as well as another reflecting EC. Analyses were also repeated in a subsample propensity matched on age and general intelligence.Results: The model showed high accuracy (73%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. EC further correlated with self-reported anxiety during the task (r=0.32, p<0.001), while DU correlated with self-reported difficulty making decisions (r=0.45, p<0.001). Compared to HCs, both DEP/ANX and SUDs showed higher DU in the propensity matched sample (t=2.16, p = .03; and t=2.88, p = .005, respectively), with analogous results in the full sample; SUDs also showed lower EC in the full sample (t=3.17, p=0.002). Limitations: This study is limited by clinical sample heterogeneity and an inability to examine learning.Conclusions: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviors in psychiatric disorders.

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A prospective examination of Axis I psychiatric disorders in the first 5 years following moderate to severe traumatic brain injury

Psychological Medicine ◽

10.1017/s0033291715002986 ◽

2016 ◽

Vol 46 (6) ◽

pp. 1331-1341 ◽

Cited By ~ 41

Author(s):

Y. Alway ◽

K. R. Gould ◽

L. Johnston ◽

D. McKenzie ◽

J. Ponsford

Keyword(s):

Risk Factors ◽

Traumatic Brain Injury ◽

Substance Use ◽

Brain Injury ◽

Anxiety Disorders ◽

Psychiatric Disorders ◽

Substance Use Disorders ◽

First Year ◽

Post Injury ◽

Over Time

BackgroundPsychiatric disorders commonly emerge during the first year following traumatic brain injury (TBI). However, it is not clear whether these disorders soon remit or persist for long periods post-injury. This study aimed to examine, prospectively: (1) the frequency, (2) patterns of co-morbidity, (3) trajectory, and (4) risk factors for psychiatric disorders during the first 5 years following TBI.MethodParticipants were 161 individuals (78.3% male) with moderate (31.2%) or severe (68.8%) TBI. Psychiatric disorders were diagnosed using the Structured Clinical Interview for DSM-IV, administered soon after injury and 3, 6 and 12 months, and 2, 3, 4 and 5 years post-injury. Disorder frequencies and generalized estimating equations were used to identify temporal relationships and risk factors.ResultsIn the first 5 years post-injury, 75.2% received a psychiatric diagnosis, commonly emerging within the first year (77.7%). Anxiety, mood and substance-use disorders were the most common diagnostic classes, often presenting co-morbidly. Many (56.5%) experienced a novel diagnostic class not present prior to injury. Disorder frequency ranged between 61.8 and 35.6% over time, decreasing by 27% [odds ratio (OR) 0.73, 95% confidence interval (CI) 0.65–0.83] with each year post-injury. Anxiety disorders declined significantly over time (OR 0.73, 95% CI 0.63–0.84), whilst mood and substance-use disorder rates remained stable. The strongest predictors of post-injury disorder were pre-injury disorder (OR 2.44, 95% CI 1.41–4.25) and accident-related limb injury (OR 1.78, 95% CI 1.03–3.07).ConclusionsFindings suggest the first year post-injury is a critical period for the emergence of psychiatric disorders. Disorder frequency declines thereafter, with anxiety disorders showing greater resolution than mood and substance-use disorders.

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Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Disorders in the All Of Us Biobank

10.1101/2021.12.02.21266700 ◽

2021 ◽

Author(s):

Peter B. Barr ◽

Tim B. Bigdeli ◽

Jacquelyn M. Meyers

Keyword(s):

Substance Use ◽

Anxiety Disorders ◽

Personality Disorders ◽

Psychiatric Disorders ◽

Substance Use Disorders ◽

Low Socioeconomic Status ◽

Large Scale ◽

Demographic Correlates ◽

Sociodemographic Correlates ◽

Demographic Covariates

ABSTRACTImportanceAll of Us is a landmark initiative for population-scale research into the etiology of psychiatric disorders and disparities across various sociodemographic categories.ObjectiveTo estimate the prevalence, comorbidity, and demographic covariates of psychiatric and substance use disorders in the All of Us biobank.Design, Setting, and ParticipantsWe estimated prevalence, overlap, and demographic correlates for psychiatric disorders derived from electronic health records in the All of Us biobank (release 5; N = 331,380)ExposuresSocial and demographic covariates.Main Outcome and MeasuresPsychiatric disorders derived from ICD10CM codes and grouped into phecodes across six broad domains: mood disorders, anxiety disorders, substance use disorders, stress-related disorders, schizophrenia, and personality disorders.ResultsThe prevalence of various disorders ranges from approximately 15% to less than 1%, with mood and anxiety disorders being the most common, followed by substance use disorders, stress-related disorders, schizophrenia, and personality disorders. There is substantial overlap among disorders, with a large portion of those with a disorder (~57%) having two or more registered diagnoses and tetrachoric correlations ranging from 0.43 – 0.74. The prevalence of disorders across demographic categories demonstrates that non-Hispanic whites, those of low socioeconomic status, women and those assigned female at birth, and sexual minorities are at greatest risk for most disorders.Conclusions and RelevanceAlthough the rates of disorders in All of Us are lower than rates for disorders in the general population, there is considerable variation, comorbidity, and differences across social groups. Large-scale resources like All of Us will prove to be invaluable for understanding the causes and consequences of psychiatric conditions. As we move towards an era of precision medicine, we must work to ensure it is delivered in an equitable manner.

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Lifetime prevalence of psychiatric disorders in South Africa

The British Journal of Psychiatry ◽

10.1192/bjp.bp.106.029280 ◽

2008 ◽

Vol 192 (2) ◽

pp. 112-117 ◽

Cited By ~ 119

Author(s):

Dan J. Stein ◽

Soraya Seedat ◽

Allen Herman ◽

Hashim Moomal ◽

Steven G. Heeringa ◽

...

Keyword(s):

Mental Health ◽

South Africa ◽

Substance Use ◽

Anxiety Disorders ◽

Psychiatric Disorders ◽

Mood Disorders ◽

Ethnic Groups ◽

Substance Use Disorders ◽

Age At Onset ◽

Lifetime Prevalence

BackgroundData on the lifetime prevalence of psychiatric disorders in South Africa are of interest, not only for the purposes of developing evidence-based mental health policy, but also in view of South Africa's particular historical and demographic circumstances.MethodA nationally representative household survey was conducted between 2002 and 2004 using the World Health Organization Composite International Diagnostic Interview (CIDI) to generate diagnoses. The data-set analysed included 4351 adult South Africans of all ethnic groups.ResultsLifetime prevalence of DSM–IV/CIDI disorders was determined for anxiety disorders (15.8%), mood disorders (9.8%), substance use disorders (13.4%) and any disorder (30.3%). Lifetime prevalence of substance use disorders differed significantly across ethnic groups. Median age at onset was earlier for substance use disorders (21 years) than for anxiety disorders (32 years) or mood disorders (37 years).ConclusionsIn comparison with data from other countries, South Africa has a particularly high lifetime prevalence of substance use disorders. These disorders have an early age at onset, providing an important target for the planning of local mental health services.

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Onset and recurrence of psychiatric disorders associated with anti-hypertensive drug classes

Translational Psychiatry ◽

10.1038/s41398-021-01444-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lucy Colbourne ◽

Sierra Luciano ◽

Paul J. Harrison

Keyword(s):

Substance Use ◽

Anxiety Disorders ◽

Sleep Disorders ◽

Psychiatric Disorders ◽

Substance Use Disorders ◽

Enzyme Inhibitor ◽

First Episode ◽

Drug Classes ◽

Β Blockers ◽

Risk Ratios

AbstractThe major anti-hypertensive (AHT) drug classes have been associated with differential risks of psychiatric disorders. However, existing data are limited largely to depression, and confounding variables have not always been controlled for. We sought to fill the evidence gap, using TriNetX Analytics, an electronic health records network. Amongst 58.6 million patients aged 18–90 years, patients prescribed a calcium channel blocker (CCB) were compared with those taking a diuretic, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or β-blocker. Cohorts were propensity score-matched for age, sex, race, and blood pressure. Over a 2-year exposure period, we measured the incidence and risk ratio of a first diagnosis (ICD-10 codes), or a recurrence, of psychotic, affective, and anxiety disorders, as well as substance use disorders and sleep disorders. Cohort sizes ranged from 33,734 to 322,814. CCBs were associated with a lower incidence of psychotic, affective, and anxiety disorders than β-blockers (risk ratios 0.69–0.99) and a higher incidence than ARBs (risk ratios 1.04–2.23) for both first and recurrent diagnoses. Comparisons of CCBs with ACEIs or diuretics showed smaller risk ratios that varied between disorders, and between first episode and recurrence. AHT classes were also associated with the incidence of substance use and sleep disorders. Results remained largely unchanged after more extensive cohort matching for additional potential confounders. In a secondary analysis, a comparison between ARBs and ACEIs showed lower rates of psychotic, affective, and substance use disorders with ARBs, but higher risks of anxiety and sleep disorders. In conclusion, AHT classes are differentially associated with the incidence of psychiatric disorders. ARBs show the most advantageous profile and β-blockers the least. The apparent beneficial effects of ARBs merit further study.

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Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Scientific Reports ◽

10.1038/s41598-021-91308-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer L. Stewart ◽

James Touthang ◽

Rayus Kuplicki ◽

...

Keyword(s):

Computational Modelling ◽

Maladaptive Behavior ◽

Group Differences ◽

Decision Uncertainty ◽

Emotional Conflict ◽

Long Term Stability ◽

Approach Avoidance ◽

Uncertainty Parameters

AbstractMaladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2–3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

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Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

10.31234/osf.io/bsuh8 ◽

2020 ◽

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer Stewart ◽

James Touthang ◽

Rayus Kuplicki ◽

...

Keyword(s):

Computational Modelling ◽

Maladaptive Behavior ◽

Group Differences ◽

Decision Uncertainty ◽

Emotional Conflict ◽

Long Term Stability ◽

Approach Avoidance ◽

Uncertainty Parameters

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes vs. reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. Here we analyzed 1-year follow-up data from a subset of the same participants (N=325) to assess parameter stability and relationships to other clinical and task measures. We also assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N=48), SUD (N=29), and DEP/ANX (N=121). Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46). Similar to previous baseline findings, these parameters correlated with multiple response time measures (ps<.001) and self-reported anxiety (r=.30, p<.001) and decision difficulty (r=.44, p<.001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

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